ElevenLabs, TwelveLabs, ThirteenLabs: why “number + Labs” became the default AI startup name

You know that moment when a totally normal browser search turns into a rabbit hole?

That happened with “ElevenLabs.” A few minutes of reading turned into a late-night pattern hunt: “TwelveLabs,” “ThirteenLabs,” then “fourteenlabs,” “fifteenlabs”… and suddenly you’re watching a parade of AI startups that all look like they were generated by the same naming spell.

At this point the real question isn’t “What is TwelveLabs?” It’s “Why does this naming scheme keep showing up in the first place?” And, more interestingly, “What does naming have to do with technical reality inside these companies?”

Let’s walk through both sides: the branding psychology and the under-the-hood way teams talk about AI systems.

The quick primer: what ElevenLabs and TwelveLabs actually do

Before naming theory, it helps to anchor the core products.

• ElevenLabs is known for speech synthesis (also called text-to-speech, or TTS)—software that converts written text into human-sounding audio. ElevenLabs describes itself as focused on advanced generative AI for audio and says it was founded in 2022. (elevenlabs.io)

• TwelveLabs positions itself as a video-native AI / video intelligence platform—the point being that it’s designed to “see and understand” video content rather than treating a video like a pile of unrelated frames. (twelvelabs.io)

Those examples also reveal a subtle theme: both companies are chasing models that work across “modalities.” In plain language, a modality is a kind of data input—audio, video, text, images. When an AI system understands more than one modality, it’s often described as multimodal.

So the “number + Labs” naming coincidence is happening in a space where multimodal models are the flashy product story.

Why “Labs” became the suffix everybody wanted

Startups don’t pick names in a vacuum. The word “Labs” carries cultural baggage from the tech world: a lab implies experiments, prototypes, and ongoing research. A TechCrunch piece on the “labs” trend described it as a branding choice that signals experimentation and innovation culture—basically, a way to look like builders rather than just marketers. (techcrunch.com)

That matters because the audience for AI startups isn’t only customers. It’s also:

• engineers (who care whether this is real research or a feature slapped onto an existing model),
• investors (who look for “seriousness” signals),
• press (who needs a short, memorable story).

“Labs” compresses a lot of that meaning into one word.

Even TwelveLabs explicitly ties its own naming to the idea of a team operating like a set of research labs working toward a shared goal. ()

So “Labs” isn’t random. It’s a semantic shortcut for “this company builds things, and the things are experimental.”

So why the numbers? The versioning instinct

Numbers feel childish—until you remember how engineers already talk.

In software and ML, numbers are everywhere:

• model versions (v1, v2, v3…)
• API versions
• research iteration cycles
• dataset revisions
• training runs

It’s common to refer to a system internally as “that model from the 12th run” or “the 11.x release.” When companies go public, they need a public-facing identity. A number turns a vague identity (“the model team”) into something that sounds like an iteration, not a one-off.

Here’s the branding-friendly translation:

• “ElevenLabs” sounds like it’s already beyond an early prototype.
• “TwelveLabs” sounds like a next step.
• “ThirteenLabs” sounds like the chain continues.

That gives a fast credibility signal: we’re not inventing AI from scratch; we’re improving on something.

And there’s a second reason numbers work: they’re distinctive. In a world where “AI,” “Data,” “Vision,” and “Cloud” are so commonly used they start blending together, a number is a rare differentiator.

A small technical analogy

Think of the name as an index into a mental list. If you search for “AI Labs” you’ll get too many matches. But “ElevenLabs” is a unique token—closer to how “GPT-4” became memorable because it’s a specific identifier.

How “number + Labs” became a default naming template

Once a template exists, it spreads.

A naming pattern can propagate for the same reason a code pattern propagates:

• early adopters validate it socially,
• copycats reduce the cost of choosing,
• founders reuse a structure that “worked somewhere else.”

TechCrunch has written about bizarre startup naming trends before, pointing out how founders often lean on recognizable naming structures to conserve decision energy and quickly get attention. ()

“Number + Labs” is exactly that kind of structure. It has:

1) a memorable identifier (the number),
2) a legitimizing suffix (“Labs”).

Put those together and the name behaves like a stack trace: it tells you which layer you’re in without needing extra explanation.

The discoverability problem (and why the web still punishes this)

Here’s where the naming strategy collides with reality.

Search engines don’t understand “intent” behind a name. They only match tokens and context.

• “Labs” is generic.
• numbers are ambiguous (eleven where? eleven what? eleven why?).

So the web ends up doing extra work: users click around to disambiguate companies that share the same naming skeleton.

That creates a weird cycle:

• companies choose the same template to gain credibility,
• but that template reduces uniqueness,
• which increases the need for extra marketing to make the company distinguishable.

The result is that you can end up with dozens of similarly styled sites and landing pages, each hoping the audience will learn their specific number.

And learning a number is a cognitive load. It’s like asking everyone to remember the exact model ID before they even care about the model’s behavior.

A surprisingly technical angle: internal system naming leaks outward

There’s a reason this pattern feels engineering-shaped: AI companies often treat their public product as a continuously iterated system.

If the business is about models, training improvements, evaluation upgrades, and model routing across use cases, then “this is our next iteration” is part of the product story.

Numbers communicate that story without forcing founders to invent new metaphors every time.

Also, AI systems are frequently layered:

• a model that understands something,
• an API or platform wrapper,
• an indexing or retrieval component,
• a UI workflow.

A numeric brand can function like a shorthand for a whole stack—even if the technical details are wildly different behind the scenes.

Doing the “name mining” yourself (a tiny regex experiment)

You don’t need a PhD in brand strategy to test the pattern. A quick text experiment can show how easily the “number + labs” shape pops out.

Example Python snippet:

import re

text = "ElevenLabs TwelveLabs 24labs.ai Thirty Labs 99Labs"

pattern = re.compile(r"\b(\d+)\s*(?:labs|lab)\b", re.IGNORECASE)

matches = pattern.findall(text)
print(matches) # ['11', '24', '99']

What this demonstrates: even in messy text, a consistent naming structure becomes machine-detectable. In other words, the pattern isn’t just “vibes.” It’s a real, consistent token shape.

That’s exactly what makes it easy for founders (and marketers, and domain squatting) to exploit.

Why buying domains like “twentyfivelabs” feels tempting

The rabbit hole naturally leads to a thought: “Should someone just buy the remaining names?”

The tempting part is straightforward—domains are scarce and easy to monetize later. But the technical snag is subtler.

A domain name that follows a template doesn’t automatically signal a real model or real product. It only signals “this might be a company.”

In AI, users increasingly judge reality by:

• demos that don’t collapse under edge cases,
• clear model behavior (latency, quality, controllability),
• integration that feels like engineering, not a slideshow.

So the name can start the conversation, but it can’t replace technical substance.

There’s also the legal/brand risk layer: similar-looking names can create trademark and confusion issues, especially when the services overlap. That isn’t a reason to panic—but it is a reason domain fantasies don’t always cash out cleanly.

The real conclusion: naming is a map of the hype cycle

“ElevenLabs, TwelveLabs, ThirteenLabs” isn’t only a comedy of coincidences. It’s a snapshot of how the AI startup ecosystem behaves when:

• the technology is fast-moving,
• the space is crowded,
• credibility signals matter as much as code,
• and founders want names that work like identifiers in a system.

The numbers make the brand feel like a version. “Labs” makes it feel like real experimentation. Together, the template promises “continuation,” “iteration,” and “serious builders.”

But it also creates a discoverability tax: users end up learning the name before learning the model.

And for something as technical and behavioral as AI, that’s a strangely inefficient starting point.

Still, the naming trend tells a clear story: when everyone is building multimodal systems, everyone also starts talking in machine-like identifiers. The branding catches up to the way engineers already think.